AI Platforms: The Ultimate 2023 Guide to Conquering the ML Lifecycle

Hey there! Are you looking to leverage the power of artificial intelligence, but struggling with the complexity of managing machine learning models? AI platforms are the solution – and in this guide, I‘ll provide everything you need to understand how AI platforms can transform your approach to AI.

What are AI Platforms and Why Do They Matter?

AI platforms provide a suite of services that support the end-to-end machine learning workflow, from data preparation to model deployment. They aim to eliminate the heavy lifting involved in developing, governing, and scaling AI applications.

Specifically, AI platforms address three core elements of the machine learning lifecycle:

Data and Integration: This provides easy access to quality training data through data management tools like Trifacta and Talend. With reliable data feeding into models, you can focus on high-value tasks instead of data wrangling.

Experimentation: AutoML tools like DataRobot and H2O Driverless AI handle repetitive tasks like feature engineering, model selection, and hyperparameter tuning so you can iterate faster.

Operations and Deployment: Governance features for model monitoring, explainability, and risk analysis. Deployment automation and infrastructure management for high-scale production.

According to Gartner, the top barriers organizations face in scaling AI are managing model degradation in production and integrating AI into business processes:

Data source: McKinsey Global AI Survey 2020

This is where AI platforms shine – they provide the enabling technologies and infrastructure to operationalize AI throughout your organization. AI platforms allow you to truly productize machine learning, instead of getting stuck in pilot purgatory.

Real-World Examples of AI Platforms in Action

AI platforms power a broad range of machine learning use cases across industries:

Fraud Detection

Banks use AI platforms like Featurespace to rapidly develop and deploy transaction monitoring models. This has reduced false positives in fraud alerts by up to 50% and decreased fraud losses.

Predictive Maintenance

Manufacturers like Grundfos are implementing AI platforms like Uptake to forecast equipment failures before they occur. By optimizing maintenance schedules, they reduce costs and avoid unplanned downtime.

Dynamic Pricing

Retailers like Marks & Spencer use AI platforms like Blue Yonder to adjust prices in real-time based on factors like demand. This has delivered over $500 million in incremental revenue.

Key Capabilities of Top AI Platforms

Leading platforms have raised significant funding (Algorithmia – $37.9M, Peltarion – $36.4M) reflecting strong market traction. Review our full list of top AI platforms to see additional options.

Should You Build In-House, Outsource, or Use an AI Platform?

While you can build custom AI infrastructure in-house, this requires substantial expertise and resources. Partnering with AI consultants provides access to skilled teams, but can be expensive for ongoing work.

AI platforms balance bespoke capabilities with turnkey tooling and infrastructure for the ML lifecycle. This allows you to focus your team on high-value tasks while benefiting from proven solutions.

Take the Next Step Towards AI Success

I hope this guide provided a clear overview of how AI platforms can help you operationalize AI at scale. To recap, they provide the enabling technologies and infrastructure to manage the end-to-end machine learning workflow. AI platforms allow you to productize models for consistent business impact, rather than getting stuck in pilot projects.

If you see an opportunity to leverage AI in your organization, I encourage you to get in touch for a free consultation. I would be happy to offer best practices tailored to your use case and requirements. Here‘s to conquering the AI mountain!